In this script we conduct the estimation for the measure_marginal approach for a single given env = ethereumjs.

PROGRAMS=pg_marginal_full5_c50_step1_shuffle SAMPLESIZE=50 NSAMPLES=4.

Expected a result file ethereumjs_pg_marginal_full5_c50_step1_shuffle_50_4.csv.

programs = read.csv(paste("stage3/", program_set_codename, ".csv", sep=""))

results = load_data_set(env, program_set_codename, measurement_codename)
# besu may have additional columns with gc stats
results = results[, c("program_id", "sample_id", "run_id", "measure_total_time_ns", "measure_total_timer_time_ns", "env")]
# TODO geth short-circuits zero length programs, resulting in zero timing somehow. Drop these more elegantly, not based on measure_total_time_ns
results = results[which(results$measure_total_time_ns != 0), ]

all_envs = c(env)
measurements = sqldf("SELECT opcode, op_count, sample_id, run_id, measure_total_time_ns, env, results.program_id
                     FROM results
                     INNER JOIN
                       programs ON(results.program_id = programs.program_id)")
measurements$opcode = factor(measurements$opcode, levels=unique(programs$opcode))
head(measurements)
##   opcode op_count sample_id run_id measure_total_time_ns        env program_id
## 1    ADD       27         0      0                326491 ethereumjs     ADD_27
## 2    ADD       27         0      1                322832 ethereumjs     ADD_27
## 3    ADD       27         0      2                328756 ethereumjs     ADD_27
## 4    ADD       27         0      3                322898 ethereumjs     ADD_27
## 5    ADD       27         0      4                326553 ethereumjs     ADD_27
## 6    ADD       27         0      5                329741 ethereumjs     ADD_27

Switch removed_outliers to FALSE to see the comparison.

boxplot(measurements[which(measurements$env == env), 'measure_total_time_ns'] ~ measurements[which(measurements$env == env), 'opcode'], las=2, outline=TRUE, log='y', main=paste(env, 'all'))

if (removed_outliers) {
  measurements = remove_compare_outliers(measurements, 'measure_total_time_ns', all_envs)
}

# For a subset of the `measurements` data frame, fits a bimodal distribution model and corrects the
# data by bringing the "top-mode" cluster down to the "bottom-mode" cluster.
correct_bimodal <- function(df) {
  mix_model = normalmixEM(df$measure_total_time_ns)
  print(summary(mix_model))
  plot(mix_model,which=2)
  mode_distance = abs(mix_model$mu[2] - mix_model$mu[1])
  mode_midpoint = (mix_model$mu[2] + mix_model$mu[1]) / 2
  over_threshold = which(df$measure_total_time_ns > mode_midpoint)
  df[over_threshold, "measure_total_time_ns"] = df[over_threshold, "measure_total_time_ns"] - mode_distance
    
  return(df)
}

# Performs the `measure_marginal` estimation procedure for a given slice of the data.
# Prints the diagnostics and plots the models.
compute_all <- function(opcode, env, plots, bimodal_opcodes, use_median) {
  if (missing(bimodal_opcodes)) {
    bimodal_opcodes = c()
  }
  if (missing(plots)) {
    plots = "scatter"
  }
  if (missing(use_median)) {
    use_median = FALSE
  }
  print(c(opcode, env))
  
  df = measurements[which(measurements$opcode==opcode & measurements$env==env),]
  
  if (opcode %in% bimodal_opcodes) {
    par(mfrow=c(1,2))
    boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
    title(main=paste(env, opcode))
    # correct_bimodal plots the second plot inside
    df = correct_bimodal(df)
  }
  
  if (use_median) {
    f = median
  } else {
    f = mean
  }
  df_mean = aggregate(measure_total_time_ns ~ op_count * env, df, f)
  
  model_mean = lm(measure_total_time_ns ~ op_count, data=df_mean)
  print(summary(model_mean))
  slope = model_mean$coefficients[['op_count']]
  stderr = summary(model_mean)$coefficients['op_count','Std. Error']
  
  if (plots == "scatter" | plots == "all") {
    par(mfrow=c(1,1))
    boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
    rounded_slope = round(slope, 3)
    rounded_p = round(summary(model_mean)$coefficients['op_count','Pr(>|t|)'], 3)
    rounded_stderr = round(stderr, 3)
    title(main=paste(env, opcode, rounded_slope, "p_value:", rounded_p, "StdErr:", rounded_stderr))
    abline(model_mean, col="red")
  }
  if (plots == "diagnostics" | plots == "all") {
    par(mfrow=c(2,2))
    plot(model_mean)
  }
  list("slope" = slope, "stderr" = stderr)
}

extract_opcodes <- function() {
  unique(measurements$opcode)
}
all_opcodes = extract_opcodes()

# initialize the data frame to hold the results
estimates = data.frame(matrix(ncol = 4, nrow = 0))
colnames(estimates) <- c('op', 'estimate_marginal_ns', 'estimate_marginal_ns_stderr', 'env')

Every sample starts with a fresh evm instance. We investigate whether the results may depend on the time from evm start - related to run_id. To avoid being overrun by the number of images, all op_count for a given run_id are are placed, so values are not centered. That should not an issue.

for (opcode in all_opcodes) {
  boxplot(measure_total_time_ns~run_id,data=measurements[measurements$opcode == opcode,], main=opcode)
}

Now we can investigate the linear regressions.

if (env == 'evmone') {
  bimodals = all_opcodes[which(grepl("PUSH", all_opcodes) & all_opcodes != "PUSH1" | all_opcodes == "JUMP")]
} else {
  bimodals = c()
}

for (opcode in all_opcodes) {
  estimate = compute_all(opcode=opcode, env=env, use_median=TRUE, bimodal_opcodes=bimodals, plots='all')
  estimates[nrow(estimates) + 1, ] = c(opcode, estimate, env)
}
## [1] "ADD"        "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1809.0  -766.0   -71.5   437.6  4018.7 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 312033.34     329.18  947.91 <0.0000000000000002 ***
## op_count       359.33      11.35   31.67 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1193 on 49 degrees of freedom
## Multiple R-squared:  0.9534, Adjusted R-squared:  0.9525 
## F-statistic:  1003 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MUL"        "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1878.2  -821.2  -210.6   512.8  2596.2 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 312184.78     322.38  968.37 <0.0000000000000002 ***
## op_count       383.13      11.11   34.48 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1168 on 49 degrees of freedom
## Multiple R-squared:  0.9604, Adjusted R-squared:  0.9596 
## F-statistic:  1189 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SUB"        "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2604.6 -1044.1  -466.8   704.7  3973.3 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 311678.59     435.62  715.49 <0.0000000000000002 ***
## op_count       363.12      15.02   24.18 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1578 on 49 degrees of freedom
## Multiple R-squared:  0.9227, Adjusted R-squared:  0.9211 
## F-statistic: 584.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DIV"        "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2459.9  -825.8  -105.2   645.4  3561.9 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 315163.8      345.2  913.03 <0.0000000000000002 ***
## op_count       309.4       11.9   26.01 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1251 on 49 degrees of freedom
## Multiple R-squared:  0.9324, Adjusted R-squared:  0.9311 
## F-statistic: 676.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SDIV"       "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2583.9  -886.4  -169.5  1000.6  4067.7 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 318632.90     427.39  745.54 <0.0000000000000002 ***
## op_count       237.01      14.73   16.09 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1549 on 49 degrees of freedom
## Multiple R-squared:  0.8408, Adjusted R-squared:  0.8376 
## F-statistic: 258.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MOD"        "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2813.5  -963.4    54.0   670.4  4095.8 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 311621.5      391.6  795.83 <0.0000000000000002 ***
## op_count       373.2       13.5   27.65 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1419 on 49 degrees of freedom
## Multiple R-squared:  0.9398, Adjusted R-squared:  0.9385 
## F-statistic: 764.6 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SMOD"       "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2069.1  -864.4   341.0   728.1  1834.9 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 314952.97     292.86 1075.45 <0.0000000000000002 ***
## op_count       313.79      10.09   31.09 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1061 on 49 degrees of freedom
## Multiple R-squared:  0.9517, Adjusted R-squared:  0.9508 
## F-statistic: 966.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "ADDMOD"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2244.6  -873.1  -232.9   896.6  6026.2 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 309736.33     388.00  798.28 <0.0000000000000002 ***
## op_count       402.04      13.37   30.06 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1406 on 49 degrees of freedom
## Multiple R-squared:  0.9486, Adjusted R-squared:  0.9475 
## F-statistic: 903.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MULMOD"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1983.3  -893.8  -125.0   913.1  3617.4 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 309676.65     344.31  899.41 <0.0000000000000002 ***
## op_count       401.34      11.87   33.82 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1248 on 49 degrees of freedom
## Multiple R-squared:  0.9589, Adjusted R-squared:  0.9581 
## F-statistic:  1144 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "EXP"        "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4522.4 -1037.4  -229.6   426.8  5302.2 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 315096.51     579.30  543.92 <0.0000000000000002 ***
## op_count      1923.39      19.97   96.32 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2099 on 49 degrees of freedom
## Multiple R-squared:  0.9947, Adjusted R-squared:  0.9946 
## F-statistic:  9278 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SIGNEXTEND" "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1611.82  -648.29   -41.95   538.48  2869.96 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 314480.701    269.549  1166.7 <0.0000000000000002 ***
## op_count       476.584      9.291    51.3 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 976.7 on 49 degrees of freedom
## Multiple R-squared:  0.9817, Adjusted R-squared:  0.9813 
## F-statistic:  2631 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "LT"         "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4744.5 -1982.5  -204.4  1576.4  6198.0 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 307579.96     708.14  434.35 <0.0000000000000002 ***
## op_count       397.28      24.41   16.28 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2566 on 49 degrees of freedom
## Multiple R-squared:  0.8439, Adjusted R-squared:  0.8407 
## F-statistic: 264.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "GT"         "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4593.6 -2036.0  -435.2  1764.5  5378.5 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 307921.35     719.02  428.25 <0.0000000000000002 ***
## op_count       397.65      24.78   16.05 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2605 on 49 degrees of freedom
## Multiple R-squared:  0.8401, Adjusted R-squared:  0.8368 
## F-statistic: 257.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SLT"        "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1906.37  -684.92   -11.52   625.82  3160.85 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 313918.149    277.146 1132.68 <0.0000000000000002 ***
## op_count       287.914      9.553   30.14 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1004 on 49 degrees of freedom
## Multiple R-squared:  0.9488, Adjusted R-squared:  0.9478 
## F-statistic: 908.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SGT"        "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2225.66  -861.95   -93.68   943.46  2332.09 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 313112.70     318.41   983.4 <0.0000000000000002 ***
## op_count       306.20      10.98    27.9 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1154 on 49 degrees of freedom
## Multiple R-squared:  0.9408, Adjusted R-squared:  0.9396 
## F-statistic: 778.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "EQ"         "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4118.9 -1946.5  -272.6  1615.2  5502.6 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 307830.9      699.2   440.2 <0.0000000000000002 ***
## op_count       407.4       24.1    16.9 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2534 on 49 degrees of freedom
## Multiple R-squared:  0.8536, Adjusted R-squared:  0.8506 
## F-statistic: 285.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "ISZERO"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1406.92  -544.50     6.88   479.72  1274.40 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 314264.802    196.926 1595.85 <0.0000000000000002 ***
## op_count       217.023      6.788   31.97 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 713.5 on 49 degrees of freedom
## Multiple R-squared:  0.9543, Adjusted R-squared:  0.9533 
## F-statistic:  1022 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "AND"        "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1941.7 -1150.1  -200.0   641.7  4372.1 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 310730.90     406.87  763.71 <0.0000000000000002 ***
## op_count       313.31      14.02   22.34 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1474 on 49 degrees of freedom
## Multiple R-squared:  0.9106, Adjusted R-squared:  0.9088 
## F-statistic: 499.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "OR"         "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1631.2  -922.3  -415.6   729.0  3603.0 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 310833.0      345.1  900.61 <0.0000000000000002 ***
## op_count       299.9       11.9   25.21 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1251 on 49 degrees of freedom
## Multiple R-squared:  0.9284, Adjusted R-squared:  0.927 
## F-statistic: 635.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "XOR"        "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2261.7  -928.7  -125.2   700.4  4609.2 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 310739.34     404.71  767.81 <0.0000000000000002 ***
## op_count       311.73      13.95   22.35 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1466 on 49 degrees of freedom
## Multiple R-squared:  0.9106, Adjusted R-squared:  0.9088 
## F-statistic: 499.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "NOT"        "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1605.6  -617.1  -125.7   360.7  4061.5 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 310825.995    268.879 1156.01 <0.0000000000000002 ***
## op_count       275.016      9.268   29.67 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 974.2 on 49 degrees of freedom
## Multiple R-squared:  0.9473, Adjusted R-squared:  0.9462 
## F-statistic: 880.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "BYTE"       "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1805.0  -892.5  -364.5   382.5  4148.4 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 311334.6      342.5  909.07 <0.0000000000000002 ***
## op_count       432.5       11.8   36.64 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1241 on 49 degrees of freedom
## Multiple R-squared:  0.9648, Adjusted R-squared:  0.9641 
## F-statistic:  1342 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SHL"        "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1784.9  -670.2  -259.6   605.9  3485.5 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 310698.98     328.21  946.66 <0.0000000000000002 ***
## op_count       359.97      11.31   31.82 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1189 on 49 degrees of freedom
## Multiple R-squared:  0.9538, Adjusted R-squared:  0.9529 
## F-statistic:  1012 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SHR"        "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2795.3 -1340.2   510.6  1154.0  2644.3 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 318133.97     411.19  773.69 <0.0000000000000002 ***
## op_count       216.36      14.17   15.27 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1490 on 49 degrees of freedom
## Multiple R-squared:  0.8263, Adjusted R-squared:  0.8227 
## F-statistic:   233 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SAR"        "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2623.5  -888.5    55.2  1024.5  3246.7 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 313910.93     369.95  848.52 <0.0000000000000002 ***
## op_count       243.92      12.75   19.13 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1340 on 49 degrees of freedom
## Multiple R-squared:  0.8819, Adjusted R-squared:  0.8795 
## F-statistic: 365.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "ADDRESS"    "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1465.88  -211.77    85.65   269.29   958.52 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 103371.555    135.957   760.3 <0.0000000000000002 ***
## op_count       485.237      4.686   103.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 492.6 on 49 degrees of freedom
## Multiple R-squared:  0.9955, Adjusted R-squared:  0.9954 
## F-statistic: 1.072e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "ORIGIN"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1720.05  -237.06   -10.85   323.70  1244.41 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 103442.0      145.1  713.06 <0.0000000000000002 ***
## op_count       481.3        5.0   96.25 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 525.6 on 49 degrees of freedom
## Multiple R-squared:  0.9947, Adjusted R-squared:  0.9946 
## F-statistic:  9265 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLER"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1275.69  -275.23    11.05   294.78   846.47 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 103584.686    134.155   772.1 <0.0000000000000002 ***
## op_count       481.307      4.624   104.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 486.1 on 49 degrees of freedom
## Multiple R-squared:  0.9955, Adjusted R-squared:  0.9954 
## F-statistic: 1.083e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLVALUE"  "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1060.01  -183.31   -18.32   199.71   889.30 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 103026.368    107.900  954.84 <0.0000000000000002 ***
## op_count       254.599      3.719   68.45 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 391 on 49 degrees of freedom
## Multiple R-squared:  0.9897, Adjusted R-squared:  0.9894 
## F-statistic:  4686 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLDATALOAD" "ethereumjs"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -6531  -1137    483   1296   2746 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 327736.8      539.7  607.28 <0.0000000000000002 ***
## op_count       261.6       18.6   14.06 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1955 on 49 degrees of freedom
## Multiple R-squared:  0.8015, Adjusted R-squared:  0.7974 
## F-statistic: 197.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLDATASIZE" "ethereumjs"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1672.00  -270.44   -38.44   376.27  1131.52 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 103628.997    150.500  688.57 <0.0000000000000002 ***
## op_count       246.498      5.188   47.52 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 545.3 on 49 degrees of freedom
## Multiple R-squared:  0.9788, Adjusted R-squared:  0.9783 
## F-statistic:  2258 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLDATACOPY" "ethereumjs"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3202.2  -556.2   109.1   612.4  2519.5 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 248710.44     376.42   660.7 <0.0000000000000002 ***
## op_count      2174.46      12.97   167.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1364 on 49 degrees of freedom
## Multiple R-squared:  0.9983, Adjusted R-squared:  0.9982 
## F-statistic: 2.809e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CODESIZE"   "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -946.5 -313.8  -94.8  315.3 1182.0 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 101376.242    139.357  727.46 <0.0000000000000002 ***
## op_count       262.240      4.803   54.59 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 504.9 on 49 degrees of freedom
## Multiple R-squared:  0.9838, Adjusted R-squared:  0.9835 
## F-statistic:  2980 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CODECOPY"   "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14449.0  -5060.3     76.1   4290.9  21881.6 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1155511.48    1943.38  594.59 <0.0000000000000002 ***
## op_count       1867.56      66.99   27.88 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7042 on 49 degrees of freedom
## Multiple R-squared:  0.9407, Adjusted R-squared:  0.9395 
## F-statistic: 777.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "GASPRICE"   "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1633.65  -214.82    29.72   385.30   928.30 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102298.961    141.059  725.22 <0.0000000000000002 ***
## op_count       225.682      4.862   46.42 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 511.1 on 49 degrees of freedom
## Multiple R-squared:  0.9778, Adjusted R-squared:  0.9773 
## F-statistic:  2154 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "RETURNDATASIZE" "ethereumjs"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1556.25  -596.50     6.49   487.19  2775.59 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 103529.755    219.700  471.23 <0.0000000000000002 ***
## op_count       240.880      7.573   31.81 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 796.1 on 49 degrees of freedom
## Multiple R-squared:  0.9538, Adjusted R-squared:  0.9529 
## F-statistic:  1012 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "RETURNDATACOPY" "ethereumjs"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2996.4 -1091.2   -58.5   538.3  4737.5 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 395577.18     486.08   813.8 <0.0000000000000002 ***
## op_count      2167.89      16.75   129.4 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1761 on 49 degrees of freedom
## Multiple R-squared:  0.9971, Adjusted R-squared:  0.997 
## F-statistic: 1.674e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "COINBASE"   "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1352.06  -344.85     0.15   428.02   980.45 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102849.116    156.730   656.2 <0.0000000000000002 ***
## op_count       833.809      5.402   154.3 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 567.9 on 49 degrees of freedom
## Multiple R-squared:  0.9979, Adjusted R-squared:  0.9979 
## F-statistic: 2.382e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "TIMESTAMP"  "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1450.4  -483.5  -111.4   545.3  1803.2 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 103201.243    217.418  474.67 <0.0000000000000002 ***
## op_count       243.848      7.494   32.54 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 787.8 on 49 degrees of freedom
## Multiple R-squared:  0.9558, Adjusted R-squared:  0.9549 
## F-statistic:  1059 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "NUMBER"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1297.8  -573.8   -18.5   479.3  1688.9 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 103380.841    203.439  508.17 <0.0000000000000002 ***
## op_count       246.087      7.012   35.09 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 737.1 on 49 degrees of freedom
## Multiple R-squared:  0.9617, Adjusted R-squared:  0.961 
## F-statistic:  1232 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DIFFICULTY" "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1699.97  -435.90   -11.74   473.71  1288.18 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 103280.36     191.76   538.6 <0.0000000000000002 ***
## op_count      1400.27       6.61   211.8 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 694.8 on 49 degrees of freedom
## Multiple R-squared:  0.9989, Adjusted R-squared:  0.9989 
## F-statistic: 4.488e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "GASLIMIT"   "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1164.9  -520.8  -200.1   600.0  2666.0 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 103306.401    220.820  467.83 <0.0000000000000002 ***
## op_count       249.226      7.611   32.74 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 800.1 on 49 degrees of freedom
## Multiple R-squared:  0.9563, Adjusted R-squared:  0.9554 
## F-statistic:  1072 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CHAINID"    "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1389.4  -719.5   113.5   524.0  2342.7 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102957.393    225.882  455.80 <0.0000000000000002 ***
## op_count       212.122      7.786   27.24 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 818.5 on 49 degrees of freedom
## Multiple R-squared:  0.9381, Adjusted R-squared:  0.9368 
## F-statistic: 742.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SELFBALANCE" "ethereumjs" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1080.09  -219.00    65.48   295.36   761.06 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102336.453    125.377  816.23 <0.0000000000000002 ***
## op_count       217.779      4.322   50.39 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 454.3 on 49 degrees of freedom
## Multiple R-squared:  0.9811, Adjusted R-squared:  0.9807 
## F-statistic:  2539 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "POP"        "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1337.14  -363.16   -23.17   498.08  1209.33 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 236808.140    168.323 1406.87 <0.0000000000000002 ***
## op_count       109.501      5.802   18.87 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 609.9 on 49 degrees of freedom
## Multiple R-squared:  0.8791, Adjusted R-squared:  0.8766 
## F-statistic: 356.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MLOAD"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3071.5 -1211.3   -14.6  1241.2  2594.2 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 328909.30     422.12  779.18 <0.0000000000000002 ***
## op_count       880.59      14.55   60.52 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1530 on 49 degrees of freedom
## Multiple R-squared:  0.9868, Adjusted R-squared:  0.9865 
## F-statistic:  3663 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MSTORE"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2099.1  -969.3  -115.4   418.7  7669.3 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 245021.13     497.41   492.6 <0.0000000000000002 ***
## op_count      2256.06      17.15   131.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1802 on 49 degrees of freedom
## Multiple R-squared:  0.9972, Adjusted R-squared:  0.9971 
## F-statistic: 1.731e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MSTORE8"    "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2940.7  -924.1  -209.1   703.2  3356.9 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 244767.40     389.05  629.15 <0.0000000000000002 ***
## op_count       821.15      13.41   61.23 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1410 on 49 degrees of freedom
## Multiple R-squared:  0.9871, Adjusted R-squared:  0.9868 
## F-statistic:  3750 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "JUMP"       "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1194.46  -286.54    52.67   340.08  1026.72 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 111055.205    146.415  758.49 <0.0000000000000002 ***
## op_count       184.778      5.047   36.61 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 530.5 on 49 degrees of freedom
## Multiple R-squared:  0.9647, Adjusted R-squared:  0.964 
## F-statistic:  1341 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "JUMPI"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3859.9 -1123.6   279.7  1019.9  3421.9 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 320239.38     433.42  738.87 <0.0000000000000002 ***
## op_count       465.23      14.94   31.14 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1570 on 49 degrees of freedom
## Multiple R-squared:  0.9519, Adjusted R-squared:  0.9509 
## F-statistic: 969.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PC"         "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -732.39 -213.89  -30.66  228.47  823.80 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 101762.468    103.819  980.19 <0.0000000000000002 ***
## op_count       236.455      3.579   66.08 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 376.2 on 49 degrees of freedom
## Multiple R-squared:  0.9889, Adjusted R-squared:  0.9887 
## F-statistic:  4366 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MSIZE"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1110.81  -262.49    14.45   290.62   949.17 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 101636.470    131.775  771.29 <0.0000000000000002 ***
## op_count       243.726      4.542   53.66 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 477.5 on 49 degrees of freedom
## Multiple R-squared:  0.9833, Adjusted R-squared:  0.9829 
## F-statistic:  2879 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "GAS"        "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1305.23  -257.02   -51.49   265.79  1122.46 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 101944.792    119.081  856.09 <0.0000000000000002 ***
## op_count       223.097      4.105   54.35 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 431.5 on 49 degrees of freedom
## Multiple R-squared:  0.9837, Adjusted R-squared:  0.9834 
## F-statistic:  2954 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "JUMPDEST"   "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -283.39  -51.79   18.91   71.61  174.81 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 20572.987     29.253   703.3 <0.0000000000000002 ***
## op_count      176.300      1.008   174.8 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 106 on 49 degrees of freedom
## Multiple R-squared:  0.9984, Adjusted R-squared:  0.9984 
## F-statistic: 3.057e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH1"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1650.4  -610.9  -213.9   480.4  2111.0 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 103374.136    231.314   446.9 <0.0000000000000002 ***
## op_count      1165.825      7.973   146.2 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 838.1 on 49 degrees of freedom
## Multiple R-squared:  0.9977, Adjusted R-squared:  0.9977 
## F-statistic: 2.138e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH2"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1822.87  -575.89   -82.61   420.50  1929.53 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102871.325    226.791   453.6 <0.0000000000000002 ***
## op_count      1223.821      7.817   156.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 821.7 on 49 degrees of freedom
## Multiple R-squared:  0.998,  Adjusted R-squared:  0.998 
## F-statistic: 2.451e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH3"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1962.06  -641.70   -64.03   344.10  2322.13 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102772.626    248.960   412.8 <0.0000000000000002 ***
## op_count      1280.351      8.581   149.2 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 902.1 on 49 degrees of freedom
## Multiple R-squared:  0.9978, Adjusted R-squared:  0.9978 
## F-statistic: 2.226e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH4"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1871.72  -675.65    82.48   345.96  2699.74 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102701.020    269.907   380.5 <0.0000000000000002 ***
## op_count      1321.616      9.303   142.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 978 on 49 degrees of freedom
## Multiple R-squared:  0.9976, Adjusted R-squared:  0.9975 
## F-statistic: 2.018e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH5"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1340.02  -512.57   -17.99   377.02  1748.62 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102789.689    197.744   519.8 <0.0000000000000002 ***
## op_count      1366.061      6.816   200.4 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 716.5 on 49 degrees of freedom
## Multiple R-squared:  0.9988, Adjusted R-squared:  0.9988 
## F-statistic: 4.017e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH6"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -880.82 -491.96  -79.26  480.24 1213.88 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 103032.657    165.965   620.8 <0.0000000000000002 ***
## op_count      1419.880      5.721   248.2 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 601.4 on 49 degrees of freedom
## Multiple R-squared:  0.9992, Adjusted R-squared:  0.9992 
## F-statistic: 6.16e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH7"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1398.51  -403.76   -21.45   291.94  1339.51 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102916.677    162.370   633.8 <0.0000000000000002 ***
## op_count      1460.043      5.597   260.9 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 588.3 on 49 degrees of freedom
## Multiple R-squared:  0.9993, Adjusted R-squared:  0.9993 
## F-statistic: 6.805e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH8"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1043.3  -360.7   -10.0   273.0  1524.7 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102966.106    138.262   744.7 <0.0000000000000002 ***
## op_count      1495.699      4.766   313.8 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 501 on 49 degrees of freedom
## Multiple R-squared:  0.9995, Adjusted R-squared:  0.9995 
## F-statistic: 9.85e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH9"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1783.97  -432.26   -90.72   465.71  1477.83 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102606.458    196.673   521.7 <0.0000000000000002 ***
## op_count      1544.550      6.779   227.8 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 712.6 on 49 degrees of freedom
## Multiple R-squared:  0.9991, Adjusted R-squared:  0.999 
## F-statistic: 5.191e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH10"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1887.41  -360.78   -57.88   346.45  1676.07 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102702.872    187.653   547.3 <0.0000000000000002 ***
## op_count      1578.120      6.468   244.0 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 679.9 on 49 degrees of freedom
## Multiple R-squared:  0.9992, Adjusted R-squared:  0.9992 
## F-statistic: 5.953e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH11"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1008.9  -489.5   -17.6   361.0  1292.1 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102819.027    154.085   667.3 <0.0000000000000002 ***
## op_count      1611.984      5.311   303.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 558.3 on 49 degrees of freedom
## Multiple R-squared:  0.9995, Adjusted R-squared:  0.9995 
## F-statistic: 9.212e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH12"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1360.85  -542.56    81.22   434.17  1619.19 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102677.674    196.720   521.9 <0.0000000000000002 ***
## op_count      1651.329      6.781   243.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 712.8 on 49 degrees of freedom
## Multiple R-squared:  0.9992, Adjusted R-squared:  0.9992 
## F-statistic: 5.931e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH13"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1268.65  -513.69   -31.75   473.41  1441.99 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102530.74     185.37   553.1 <0.0000000000000002 ***
## op_count      1689.93       6.39   264.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 671.7 on 49 degrees of freedom
## Multiple R-squared:  0.9993, Adjusted R-squared:  0.9993 
## F-statistic: 6.995e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH14"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1659.1  -341.9    -7.8   351.6  1421.4 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102777.895    165.817   619.8 <0.0000000000000002 ***
## op_count      1731.311      5.716   302.9 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 600.8 on 49 degrees of freedom
## Multiple R-squared:  0.9995, Adjusted R-squared:  0.9995 
## F-statistic: 9.176e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH15"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1089.43  -412.80   -78.74   415.06  1273.19 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102564.817    158.184   648.4 <0.0000000000000002 ***
## op_count      1770.989      5.452   324.8 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 573.2 on 49 degrees of freedom
## Multiple R-squared:  0.9995, Adjusted R-squared:  0.9995 
## F-statistic: 1.055e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH16"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -966.50 -466.30  -64.15  326.10 1329.93 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102802.305    161.453   636.7 <0.0000000000000002 ***
## op_count      1816.071      5.565   326.3 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 585 on 49 degrees of freedom
## Multiple R-squared:  0.9995, Adjusted R-squared:  0.9995 
## F-statistic: 1.065e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH17"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -943.55 -285.23  -18.69  369.83 1153.72 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102893.477    130.383   789.2 <0.0000000000000002 ***
## op_count      1853.906      4.494   412.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 472.4 on 49 degrees of freedom
## Multiple R-squared:  0.9997, Adjusted R-squared:  0.9997 
## F-statistic: 1.702e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH18"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1207.75  -468.79    35.23   303.83  1262.27 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102715.088    150.041   684.6 <0.0000000000000002 ***
## op_count      1898.360      5.172   367.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 543.7 on 49 degrees of freedom
## Multiple R-squared:  0.9996, Adjusted R-squared:  0.9996 
## F-statistic: 1.347e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH19"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1284.73  -426.84    -6.73   288.24  1633.42 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102829.406    184.904   556.1 <0.0000000000000002 ***
## op_count      1929.140      6.373   302.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 670 on 49 degrees of freedom
## Multiple R-squared:  0.9995, Adjusted R-squared:  0.9995 
## F-statistic: 9.162e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH20"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1306.17  -331.63    40.15   372.58  1413.41 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102664.429    148.778   690.0 <0.0000000000000002 ***
## op_count      1978.026      5.128   385.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 539.1 on 49 degrees of freedom
## Multiple R-squared:  0.9997, Adjusted R-squared:  0.9997 
## F-statistic: 1.488e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH21"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1176.92  -385.71    67.59   275.65  1455.22 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102733.778    153.085   671.1 <0.0000000000000002 ***
## op_count      2013.705      5.277   381.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 554.7 on 49 degrees of freedom
## Multiple R-squared:  0.9997, Adjusted R-squared:  0.9997 
## F-statistic: 1.456e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH22"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1051.2  -376.2   -56.9   289.4  1746.8 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102807.486    172.571   595.7 <0.0000000000000002 ***
## op_count      2060.493      5.948   346.4 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 625.3 on 49 degrees of freedom
## Multiple R-squared:  0.9996, Adjusted R-squared:  0.9996 
## F-statistic: 1.2e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH23"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1258.84  -396.32    13.04   370.52  1399.18 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102726.195    170.164   603.7 <0.0000000000000002 ***
## op_count      2088.540      5.865   356.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 616.6 on 49 degrees of freedom
## Multiple R-squared:  0.9996, Adjusted R-squared:  0.9996 
## F-statistic: 1.268e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH24"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1028.43  -446.35   -59.22   253.87  1664.75 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102941.774    159.126   646.9 <0.0000000000000002 ***
## op_count      2134.363      5.485   389.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 576.6 on 49 degrees of freedom
## Multiple R-squared:  0.9997, Adjusted R-squared:  0.9997 
## F-statistic: 1.514e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH25"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1553.80  -442.94    40.19   365.23  1576.50 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102782.558    188.356   545.7 <0.0000000000000002 ***
## op_count      2165.911      6.492   333.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 682.5 on 49 degrees of freedom
## Multiple R-squared:  0.9996, Adjusted R-squared:  0.9996 
## F-statistic: 1.113e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH26"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1672.89  -363.24    86.77   375.31  1628.31 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102664.954    180.287   569.5 <0.0000000000000002 ***
## op_count      2218.256      6.214   357.0 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 653.2 on 49 degrees of freedom
## Multiple R-squared:  0.9996, Adjusted R-squared:  0.9996 
## F-statistic: 1.274e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH27"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1035.08  -435.32   -86.96   240.28  2274.90 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 103022.4      200.2   514.7 <0.0000000000000002 ***
## op_count      2244.6        6.9   325.3 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 725.3 on 49 degrees of freedom
## Multiple R-squared:  0.9995, Adjusted R-squared:  0.9995 
## F-statistic: 1.058e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH28"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1951.2  -316.2    96.4   298.0  1205.0 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102913.149    178.257   577.3 <0.0000000000000002 ***
## op_count      2272.573      6.144   369.9 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 645.9 on 49 degrees of freedom
## Multiple R-squared:  0.9996, Adjusted R-squared:  0.9996 
## F-statistic: 1.368e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH29"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1596.55  -400.29   -49.39   399.77  1506.02 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102859.517    191.272   537.8 <0.0000000000000002 ***
## op_count      2305.738      6.593   349.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 693 on 49 degrees of freedom
## Multiple R-squared:  0.9996, Adjusted R-squared:  0.9996 
## F-statistic: 1.223e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH30"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1232.67  -364.61    70.57   443.41  1429.37 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 102982.182    158.738   648.8 <0.0000000000000002 ***
## op_count      2342.760      5.472   428.2 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 575.2 on 49 degrees of freedom
## Multiple R-squared:  0.9997, Adjusted R-squared:  0.9997 
## F-statistic: 1.833e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH31"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1145.8  -349.8    10.7   342.4  1072.2 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 103182.322    153.519   672.1 <0.0000000000000002 ***
## op_count      2390.967      5.292   451.8 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 556.3 on 49 degrees of freedom
## Multiple R-squared:  0.9998, Adjusted R-squared:  0.9998 
## F-statistic: 2.042e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH32"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1517.65  -326.95    57.05   379.86  1127.91 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 103330.149    162.159   637.2 <0.0000000000000002 ***
## op_count      2417.828      5.589   432.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 587.6 on 49 degrees of freedom
## Multiple R-squared:  0.9997, Adjusted R-squared:  0.9997 
## F-statistic: 1.871e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP1"       "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -4128  -1624   -276   1203   5897 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 315462.39     568.29  555.11 <0.0000000000000002 ***
## op_count       307.38      19.59   15.69 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2059 on 49 degrees of freedom
## Multiple R-squared:  0.834,  Adjusted R-squared:  0.8306 
## F-statistic: 246.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP2"       "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -3404  -1439   -145   1058   5726 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 315798.26     547.97  576.31 <0.0000000000000002 ***
## op_count       296.70      18.89   15.71 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1985 on 49 degrees of freedom
## Multiple R-squared:  0.8343, Adjusted R-squared:  0.8309 
## F-statistic: 246.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP3"       "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4142.7 -1279.9  -185.9   954.4  5457.5 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 315922.54     593.13  532.64 <0.0000000000000002 ***
## op_count       295.52      20.44   14.46 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2149 on 49 degrees of freedom
## Multiple R-squared:   0.81,  Adjusted R-squared:  0.8062 
## F-statistic: 208.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP4"       "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5775.5 -1348.7  -188.8  1151.0  6858.2 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 315922.50     595.80  530.25 <0.0000000000000002 ***
## op_count       292.82      20.54   14.26 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2159 on 49 degrees of freedom
## Multiple R-squared:  0.8058, Adjusted R-squared:  0.8018 
## F-statistic: 203.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP5"       "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4051.8 -1109.9  -511.8   575.2  6524.0 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 315306.05     611.06  516.00 <0.0000000000000002 ***
## op_count       316.47      21.06   15.03 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2214 on 49 degrees of freedom
## Multiple R-squared:  0.8217, Adjusted R-squared:  0.818 
## F-statistic: 225.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP6"       "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4816.3 -1734.5  -192.2  1032.7  7406.5 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 315609.94     659.17  478.80 <0.0000000000000002 ***
## op_count       288.40      22.72   12.69 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2388 on 49 degrees of freedom
## Multiple R-squared:  0.7668, Adjusted R-squared:  0.762 
## F-statistic: 161.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP7"       "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2599.0  -568.2    82.9   644.2  3635.5 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 318991.65     302.80 1053.46 <0.0000000000000002 ***
## op_count       236.61      10.44   22.67 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1097 on 49 degrees of freedom
## Multiple R-squared:  0.9129, Adjusted R-squared:  0.9112 
## F-statistic: 513.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP8"       "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5373.8  -548.2   254.1   632.6  3316.2 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 321765.83     365.09  881.34 <0.0000000000000002 ***
## op_count       216.75      12.58   17.22 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1323 on 49 degrees of freedom
## Multiple R-squared:  0.8582, Adjusted R-squared:  0.8554 
## F-statistic: 296.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP9"       "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3256.2 -1134.6  -505.6   809.9  5600.7 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 315431.02     531.95  592.97 <0.0000000000000002 ***
## op_count       303.31      18.34   16.54 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1927 on 49 degrees of freedom
## Multiple R-squared:  0.8481, Adjusted R-squared:  0.845 
## F-statistic: 273.6 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP10"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4097.2 -1010.6  -364.3   759.1  5928.7 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 316224.02     506.68  624.11 <0.0000000000000002 ***
## op_count       283.39      17.46   16.23 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1836 on 49 degrees of freedom
## Multiple R-squared:  0.8431, Adjusted R-squared:  0.8399 
## F-statistic: 263.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP11"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4234.5 -1662.1  -485.4  1945.6  5927.5 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 320439.70     678.46  472.30 <0.0000000000000002 ***
## op_count       287.84      23.39   12.31 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2458 on 49 degrees of freedom
## Multiple R-squared:  0.7556, Adjusted R-squared:  0.7506 
## F-statistic: 151.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP12"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4015.2 -1387.5  -126.2   685.1  6303.9 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 315688.77     555.91  567.88 <0.0000000000000002 ***
## op_count       307.43      19.16   16.04 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2014 on 49 degrees of freedom
## Multiple R-squared:  0.8401, Adjusted R-squared:  0.8368 
## F-statistic: 257.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP13"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3197.3 -1257.5  -354.1   494.8  7549.4 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 318701.19     562.61  566.47 <0.0000000000000002 ***
## op_count       281.56      19.39   14.52 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2039 on 49 degrees of freedom
## Multiple R-squared:  0.8114, Adjusted R-squared:  0.8075 
## F-statistic: 210.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP14"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3551.8  -666.1   -33.3   491.4  4108.5 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 319651.26     311.89 1024.90 <0.0000000000000002 ***
## op_count       208.42      10.75   19.39 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1130 on 49 degrees of freedom
## Multiple R-squared:  0.8847, Adjusted R-squared:  0.8823 
## F-statistic: 375.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP15"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3428.6 -1135.8  -205.7  1041.3  3644.5 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 315415.75     454.87  693.41 <0.0000000000000002 ***
## op_count       318.89      15.68   20.34 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1648 on 49 degrees of freedom
## Multiple R-squared:  0.8941, Adjusted R-squared:  0.8919 
## F-statistic: 413.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP16"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4537.5 -1151.9  -541.2  1072.0  5019.5 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 325763.5      638.2  510.40 <0.0000000000000002 ***
## op_count       269.0       22.0   12.23 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2313 on 49 degrees of freedom
## Multiple R-squared:  0.7531, Adjusted R-squared:  0.7481 
## F-statistic: 149.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP1"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -962.64 -318.43   -8.43  263.01 1295.91 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 234398.589    137.548 1704.12 <0.0000000000000002 ***
## op_count       145.184      4.741   30.62 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 498.4 on 49 degrees of freedom
## Multiple R-squared:  0.9503, Adjusted R-squared:  0.9493 
## F-statistic: 937.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP2"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1418.78  -270.21    23.12   365.77  1178.55 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 234163.448    141.435 1655.62 <0.0000000000000002 ***
## op_count       146.052      4.875   29.96 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 512.5 on 49 degrees of freedom
## Multiple R-squared:  0.9482, Adjusted R-squared:  0.9472 
## F-statistic: 897.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP3"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1166.29  -323.68   -11.37   338.28  1193.67 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 234124.914    156.494 1496.06 <0.0000000000000002 ***
## op_count       153.692      5.394   28.49 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 567 on 49 degrees of freedom
## Multiple R-squared:  0.9431, Adjusted R-squared:  0.9419 
## F-statistic: 811.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP4"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -792.92 -394.66  -15.05  329.49 1035.19 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 234288.364    126.454 1852.76 <0.0000000000000002 ***
## op_count       155.836      4.359   35.75 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 458.2 on 49 degrees of freedom
## Multiple R-squared:  0.9631, Adjusted R-squared:  0.9623 
## F-statistic:  1278 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP5"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -859.01 -303.52  -59.54  254.12 1228.95 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 234324.551    117.032 2002.22 <0.0000000000000002 ***
## op_count       151.230      4.034   37.49 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 424 on 49 degrees of freedom
## Multiple R-squared:  0.9663, Adjusted R-squared:  0.9656 
## F-statistic:  1405 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP6"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -966.88 -305.56   21.25  351.79 1231.56 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 236870.440    131.876 1796.16 <0.0000000000000002 ***
## op_count       144.270      4.546   31.74 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 477.8 on 49 degrees of freedom
## Multiple R-squared:  0.9536, Adjusted R-squared:  0.9527 
## F-statistic:  1007 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP7"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -995.08 -332.44   15.99  192.14 1263.92 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 239101.451    136.305 1754.17 <0.0000000000000002 ***
## op_count       140.142      4.698   29.83 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 493.9 on 49 degrees of freedom
## Multiple R-squared:  0.9478, Adjusted R-squared:  0.9467 
## F-statistic: 889.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP8"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1488.08  -311.83   -61.08   395.92  1643.92 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 234256.579    165.986 1411.31 <0.0000000000000002 ***
## op_count       145.167      5.721   25.37 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 601.4 on 49 degrees of freedom
## Multiple R-squared:  0.9293, Adjusted R-squared:  0.9278 
## F-statistic: 643.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP9"      "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -799.22 -264.58  -19.26  250.19 1279.49 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 234459.51     114.89 2040.71 <0.0000000000000002 ***
## op_count       150.54       3.96   38.01 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 416.3 on 49 degrees of freedom
## Multiple R-squared:  0.9672, Adjusted R-squared:  0.9665 
## F-statistic:  1445 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP10"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1044.91  -426.16     9.95   368.81  2006.26 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 242419.743    165.864 1461.56 <0.0000000000000002 ***
## op_count       147.551      5.717   25.81 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 601 on 49 degrees of freedom
## Multiple R-squared:  0.9315, Adjusted R-squared:  0.9301 
## F-statistic: 666.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP11"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1128.78  -292.75    47.57   263.98  1358.81 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 234400.192    132.469 1769.47 <0.0000000000000002 ***
## op_count       140.941      4.566   30.87 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 480 on 49 degrees of freedom
## Multiple R-squared:  0.9511, Adjusted R-squared:  0.9501 
## F-statistic: 952.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP12"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1269.87  -343.70    45.05   384.73  1413.01 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 236867.98     156.95  1509.2 <0.0000000000000002 ***
## op_count       137.95       5.41    25.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 568.7 on 49 degrees of freedom
## Multiple R-squared:  0.9299, Adjusted R-squared:  0.9285 
## F-statistic: 650.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP13"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1159.59  -305.16   -66.35   300.04  1357.09 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 236674.908    163.373 1448.68 <0.0000000000000002 ***
## op_count       143.839      5.631   25.54 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 592 on 49 degrees of freedom
## Multiple R-squared:  0.9301, Adjusted R-squared:  0.9287 
## F-statistic: 652.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP14"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1287.10  -337.03     1.29   403.53  1316.44 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 234149.557    161.145 1453.03 <0.0000000000000002 ***
## op_count       145.826      5.555   26.25 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 583.9 on 49 degrees of freedom
## Multiple R-squared:  0.9336, Adjusted R-squared:  0.9323 
## F-statistic: 689.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP15"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1205.1  -446.1   106.8   476.2  1589.5 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 247972.985    192.111 1290.78 <0.0000000000000002 ***
## op_count       148.410      6.622   22.41 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 696.1 on 49 degrees of freedom
## Multiple R-squared:  0.9111, Adjusted R-squared:  0.9093 
## F-statistic: 502.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP16"     "ethereumjs"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1290.20  -390.01    84.99   420.15  1299.42 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 242344.583    157.426 1539.42 <0.0000000000000002 ***
## op_count       143.317      5.426   26.41 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 570.4 on 49 degrees of freedom
## Multiple R-squared:  0.9344, Adjusted R-squared:  0.933 
## F-statistic: 697.6 on 1 and 49 DF,  p-value: < 0.00000000000000022

Export the results

write.csv(estimates, paste0("../../local/", env, "_marginal_estimated_cost.csv"), quote=FALSE, row.names=FALSE)